Impact of the Production Mix Preservation on the ORV Problem

Bautista, Joaquı́n; Cano, Alberto; Alfaro, Rocío; Batalla, Cristina

doi:10.1007/978-3-642-40643-0_26

Cited by 6 publications

(7 citation statements)

References 6 publications

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“…Because of this, we have incorporated into the MMSP-W a set of constraints that favor the production regularity, i.e. preserving the production mix over the manufacturing sequence [6,10].…”

Section: Preliminaresmentioning

confidence: 99%

“…Indeed these objectives often respond to several concerns about production [3]. Among them, there are: (o.1) maximise the useless time, completing the maximum number of units and therefore reducing the unnecessary waitings [4]; (o.2) maximise the level of satisfaction of the set of constraints, which are related with spatial components of the products [5]; and (o.3) maintain constant the manufacturing rate of products and the component consumption rate in order to minimise the maximum levels of component stocks [6]. …”

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confidence: 99%

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GRASP for sequencing mixed models in an assembly line with work overload, useless time and production regularity

2015

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Abstract.A GRASP algorithm is presented for solving a sequencing problem in a mixed-model assembly line. The problem is focused on obtaining a manufacturing sequence that completes the greatest possible amount of required work and fulfils the production regularity property. The implemented GRASP algorithm is compared with other resolution procedures by means of instances from a case study linked to the Nissan's engine plant in Barcelona.Keywords: GRASP; Sequencing; Mixed-model assembly line; Production mix preservation. PreliminaresA mixed-model assembly line is able to manufacture several variants of the same product (e.g. engines for SUVs (Sport Utility Vehicle) and different types of vans) without physical changes at workstations and without significant setup times between consecutive different units. This type of assembly lines presents two categories of problems that are solved traditionally sequentially: (1) balancing problems [1], and (2) product sequencing problems [2]. The first problem type consists of assigning a set of tasks (relating to the product assembly) into a set of workstations arranged in series with the maximum efficiency as possible and fulfilling a set of conditions. Once solved the first problem and given a demand plan and the time to perform the said plan, the second type of problems consists of establishing the manufacturing order of products regarding one or more criteria. The objectives taken into account when the units are sequenced are not necessarily mutually exclusive. Indeed these objectives often respond to several concerns about production [3]. Among them, there are: (o.1) maximise the useless time, completing the maximum number of units and therefore reducing the unnecessary waitings [4]; (o.2) maximise the level of satisfaction of the set of constraints, which are related with spatial components of the products [5]; and (o.3) maintain constant the manufacturing rate of products and the component consumption rate in order to minimise the maximum levels of component stocks [6].

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“…Because of this, we have incorporated into the MMSP-W a set of constraints that favor the production regularity, i.e. preserving the production mix over the manufacturing sequence [6,10].…”

Section: Preliminaresmentioning

confidence: 99%

mentioning

confidence: 99%

GRASP for sequencing mixed models in an assembly line with work overload, useless time and production regularity

2015

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“…For that reason, we resort to the non-regularity functions from typical problems of the manufacturing ideology JIT [6,8], such as the Product Rate Variation Problem (PRVP) or the Output Rate Variation Problem (ORVP). This allows us to obtain a metric that enables to discriminate solutions regarding the production regularity.…”

Section: Computational Experiment Case Studymentioning

confidence: 99%

Free and regular mixed-model sequences by a linear program-assisted hybrid algorithm GRASP-LP

Bautista

Alfaro-Pozo

2017

Prog Artif Intell

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Abstract.A linear program-assisted hybrid algorithm (GRASP-LP) is presented to solve a mixed-model sequencing problem in an assembly line. The issue of the problem is to obtain manufacturing sequences of product models with the minimum work overload, allowing the free interruption of operations at workstations and preserving the production mix. The implemented GRASP-LP is compared with other procedures through a case study linked with the Nissan' Engine Plant from Barcelona.Keywords: GRASP; Linear programming; Sequencing; Mixed-model assembly lines: Production mix preservation PreliminariesFlexibility is the paradigm of the vast majority of the current production systems. Today production systems must be able to manufacture different versions of a product without physical changes at modules or workstations and with negligible setup times between different-type consecutive units; furthermore, they must respond quickly to any variation in the production plan. For this reason flexibility is what makes it important the sequencing problem.This flexibility is crucial at many manufacturing sectors, such as the Automotive, where production is carried on mixed-model assembly lines and the product mix changes frequently. This leads to the two main problems of this type of assembly lines: the balancing problem and the sequencing problem.Balancing problem appears in first place and it consists of assigning efficiently the set of assembly tasks for a product into the set of workstations arranged in series. The resulting line's configuration must meet the coherent order of tasks, and the set of restrictions linked with the task-attributes, such as the processing time, the required space and the involved risk [1].Once the line is configured and the demand plan is defined, the sequencing problem appears. This problem focuses on determining the manufacturing order of prod-

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“…The imposition of these restrictions on the sequences leads to a set of properties which are defined in [29]. These properties are the following:…”

Section: Properties From the Production MIX Restrictions (Pmr)mentioning

confidence: 99%

“…Similar to [29], in this work, we measure the non-regularity of a sequence using the following quadratic function (15):…”

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confidence: 99%

A hybrid dynamic programming for solving a mixed-model sequencing problem with production mix restriction and free interruptions

2016

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Abstract. In this article, we propose a hybrid procedure based on bounded dynamic programming (BDP) assisted by linear programming (LP) to solve the mixed-model sequencing problem with workload minimization (MMSP-W) with serial workstations, free interruption of the operations and with production mix restrictions. We performed a computational experiment with 23 instances related to a case study of the Nissan powertrain plant located in Barcelona. The results of our proposal are compared with those obtained by Mixed Integer Linear Programming (MILP).

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Impact of the Production Mix Preservation on the ORV Problem

Cited by 6 publications

References 6 publications

GRASP for sequencing mixed models in an assembly line with work overload, useless time and production regularity

GRASP for sequencing mixed models in an assembly line with work overload, useless time and production regularity

Free and regular mixed-model sequences by a linear program-assisted hybrid algorithm GRASP-LP

A hybrid dynamic programming for solving a mixed-model sequencing problem with production mix restriction and free interruptions

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